Chaotic oppositional-based whale optimization to train a feed forward neural network.

The feed forward neural network (FNN) has a significant breakthrough in recent times in solving different real-life complex problems. The success of FNN is primarily attributed to its architecture, the optimization technique used, and the tuning of hyper-parameters to identify different patterns in data. This study tries to find an optimization technique that will play a vital role in an FNN to get better results. Whale optimization algorithm (WOA) is one of the popular nature-inspired optimization techniques used to solve real-time sciences and engineering problems. A new optimization approach namely chaotic oppositional-based whale optimization algorithm (COWOA) is developed in this paper, for the first time, to discuss the FNN problems of four different dataset by integrating chaotic function and oppositional-based learning with WOA. Simulation results are presented to validate the success of the suggested COWOA approach and demonstrate its dominance over basic WOA, chaotic WOA (CWOA), particle swarm optimization (PSO), Adam optimization algorithm (AOA), and stochastic gradient descent (SGD) for both unimodal and multimodal dataset problems. The comparative analysis with other optimization techniques validates its superiority and robustness on four standard dataset of FNN problem. Furthermore, to validate the performance of the proposed algorithm, a statistical analysis is performed. The outcome of the statistical results validates the superiority and acceptability of COWOA over other optimization methods. [ABSTRACT FROM AUTHOR]